Bibliographic record
Abstract
In present study, Tabriz Mellat Bank head office building was specifically modeled using the design builder software, and, then Bio PCM (Bio phase-change) materials were used in its external walls.The present study consists of three sections.In the first section, the phase-change materials were used in the inner layer of the wall and each of them were simulated.In the second section, the phase-change materials were used in the middle layer, and finally, in the third section, all the phase-change materials were used in the outer layer of the building.Simulation was carried out using the energy simulation engine (Energy plus software).In this software, simulation is performed in hours using the weather data of Tabriz, which is in the TMY2 format.After obtaining annual energy consumption in each of the above-mentioned scenarios, in each layer, the best type of phase-change material was selected, and then, among them, the most effective material was selected.Simulation of 12 types of phase-change materials in three layers of the wall showed that the best result was obtained by using the m91q29 material with an annual energy consumption of 103706 kWh and the air source unitary heat pump and the worst result was obtained using the m27q29 material in the middle layer with an annual energy consumption of 13,216.1 kwh and a unitary heat pump.Moreover, simulation of different heat pumps showed that two air-to-water heat pump with integrated boiler and without boiler with an annual energy consumption of 37818.31kWh had the best result and the worst results were observed in using GSHP unitary Water to air with an annual energy consumption of 111494.84kwh.Considering the optimal PCM and heat pump, it was conclude that the best state is the case in which the m91q29 material is placed in the inner layer of the wall, and one of the two ASHP Air-to-water Heat Pump, Integrated Boiler, Water Convector and ASHP Air-to-water Heat Pump, Water Convector provides cooling or heating.Moreover, the weakest state is the case in which the m27q29 material is used in the middle layer and the water-to-air geothermal heat pump is used.Given the annual power consumption in the simple state (92438.79kWh) and the optimal state (21861 kWh), it was concluded that using the optimal PCM and the most desired heat pump results in 76% reduction in power consumption, indicating the significant effects of these two parameters.Moreover, according to the numerical values of the annual power consumed by the heat pump in the simple state (92438.79kWh) and the weakest state (88569 kWh), only 4.1% reduction in power consumption was observed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.996 | 0.991 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".